AI In Assessing Environmental Risk Factors

AI holds incredible promise and potential, but something I find especially inspiring is how it can help us solve some of the biggest environmental challenges we face today, such as the global wildfires.    In Canada, Microsoft is working with the Government of Alberta and AltaML on a new AI tool which leverages machine learning to predict the risk of new wildfires by region and even by hour. Capable of analyzing tens of thousands of data points, it provides insights that help firefighting agencies plan ahead, allocate resources efficiently, and prevent fires from spreading out of control – and it’s showing great promise for other wildfire-prone regions around the world.     Learn more here: https://aka.ms/AAmlsim 

🌍 Excited to share our new research published in Environmental Science & Technology (ES&T), a journal with an impact factor of 10.9. Our paper introduces Parakeet, an AI solution that combines large language models with semantic matching to automatically recommend emission factors for life cycle assessments - a critical but time-consuming step in carbon footprint calculations. 📊 Organizations often struggle with inconsistent manual mapping processes that can take weeks of expert time and lack clear documentation for audits. Our algorithm achieves 87% accuracy in fully automated matching and 93% accuracy with human review, while providing transparent, verifiable justifications for its recommendations. 🤖 This development significantly accelerates carbon accounting, especially for complex Scope 3 emissions calculations across supply chains. By streamlining this process, we're enabling organizations of all sizes to more efficiently measure and manage their environmental impact as they work toward net-zero emissions targets. This research represents a major step forward in scaling up carbon footprint assessments across industries. 👥 Work with my wonderful colleagues at Amazon: Fahimeh Ebrahimi, Ph.D. Nina Domingo Gargeya Vunnava Abu-Zaher F. Somasundari Ramalingam Shikha Gupta Anran Wang Harsh Gupta Domenic Belcastro Kellen Axten Jeremie Hakian Jared Kramer Aravind Srinivasan and Qingshi Tu, PhD 📄 Link to paper (open access for a limited time, requires sign in): https://lnkd.in/g4fYzdFb 📄 Open link to previous version of paper: https://lnkd.in/dUtva7Nh #amazonscience #sustainability #carbonfootprint #ai

Wildfires destroyed over 30 million acres globally in 2023. Now, a groundbreaking AI model from the ECMWF is changing how we fight back. Their new “Probability of Fire” (PoF) model doesn’t rely on flashier algorithms; it thrives on better data. By integrating real-time weather patterns, vegetation conditions, and human activity, PoF offers wildfire risk predictions that are not only more accurate, but also more accessible to smaller agencies with limited resources. This is a perfect example of how better data > better algorithms when it comes to real-world impact. As climate change accelerates the frequency and severity of wildfires, tools like PoF could be game changers in helping communities prepare, respond, and ultimately save lives. #AI #ClimateTech #WildfirePrevention